Identifying hepatocellular carcinoma patients with survival benefits from surgery combined with chemotherapy: based on machine learning model

BACKGROUND: Hepatocellular carcinoma (HCC) is still fatal even after surgical resection. The purpose of this study was to analyze the prognostic factors of 5-year survival rate and to establish a model to identify HCC patients with gain of surgery combined with chemotherapy. METHODS: All patients with HCC after surgery from January 2010 to December 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic analysis were used to analyze the prognostic factors of patients, and the risk prediction model of 5-year survival rate of HCC patients was established by classical decision tree method. Propensity score matching was used to eliminate the confounding factors of whether to receive chemotherapy in high-risk group or low-risk group. RESULTS: One-thousand six-hundred twenty-five eligible HCC patients were included in the study. Marital status, α-fetoprotein (AFP), vascular infiltration, tumor size, number of lesions, and grade were independent prognostic factors affecting the 5-year survival rate of HCC patients. The area under the curve of the 5-year survival risk prediction model constructed from the above variables was 0.76, and the classification accuracy, precision, recall, and F1 scores were 0.752, 0.83, 0.842, and 0.836, respectively. High-risk patients classified according to the prediction model had better 5-year survival rate after chemotherapy, while there was no difference in 5-year survival rate between patients receiving chemotherapy and patients not receiving chemotherapy in the low-risk group. CONCLUSIONS: The 5-year survival risk prediction model constructed in this study provides accurate survival prediction information. The high-risk patients determined according to the prediction model may benefit from the 5-year survival rate after combined chemotherapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12957-022-02837-2.